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  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "# -*- coding: utf-8 -*-\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import os\n",
    "import pathlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "shared_dir = \"/gpfs/scratch/chgwang/XI/DataBase/Model_figure\"\n",
    "csv_path = \"/gpfs/scratch/chgwang/XI/DataBase/Model_1d/evaluation.csv\"\n",
    "df = pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.close()\n",
    "plt.figure()\n",
    "plt.plot(df.index, df.loc[:, \"tra_loss\"], label=\"training loss\")\n",
    "plt.plot(df.index, df.loc[:, \"val_loss\"], label=\"validation loss\")\n",
    "plt.xticks(np.arange(0,101,5))\n",
    "plt.title(\"model loss\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.legend()\n",
    "fig_path = os.path.join(shared_dir, \"model_loss_100.png\")\n",
    "plt.savefig(fig_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}